Development of self-regulated learning: a longitudinal study on academic performance in undergraduate science

Figures & data

Figure 1. Timeline for deployment of the SRL questionnaire and knowledge survey to a cohort of first-year agriculture students (n = 213) enrolled in a core agricultural science subject in semester 1, 2018. Details of periods for collection of Australian Tertiary Admissions Rank (ATAR) and average semester mark shown.

Table 1. Details of study strategies used by students to aid learning in higher education with samples from the short-answer component of the SRL questionnaire.

Strategy	Description	Sample
Reviewing records	Re-watching lectures or reading, highlighting & annotating notes	‘ I use cue cards to try and remember content and revise over key terms’
Keeping records & monitoring	Copying or paraphrasing material by transcription	‘Going through and writing lecture notes: understand and summarize slides and concepts’
Organising & transforming	Consolidating records by creating diagrams, tables and cheat sheets	‘ … making posters, printing a blank diagram annotating it as much as possible, typing a detailed set of notes and then making summary pages from that’
Seeking social assistance	Interactive study with peers, teachers, and friends	‘Connect with my friends. Form a study group to study with each other’.
Self-testing	Writing and/or completing practice questions	‘Lots of practice questions and previous exam papers’
Environmental structuring	Adapting or modifying one’s study routine or environment	‘Make lists of things I need to do every day. This helps me to stay organised. Start assignments early and complete small components at different times’.
Critical thinking	Analysing and evaluating material by monitoring cognitive processes	‘If lost in the concept, I try to apply life situations to the concept to better understand’.
Outsourcing material	Seeking material from outside the course	‘Consult other resources: Kahn Academy, YouTube’.

Responses coded into categories adapted from Nota, Soresi, and Zimmerman (2004) and refined based on responses to Q38. ‘Briefly explain any other strategies you have used to aid your study’ (see Appendix 1).

Table 2. Proportion of students who (A) reported strategies as being most effective in aiding their learning and (B) intended on changing strategies for future assessment at the end of semester 1, 2018 (T2) and at start semester 2, 2020 (T3).

	2018	2020
A. What do you feel has been the most effective study strategy you have used?
1. Reviewing records	25% (15/60)	22% (7/32)
2. Keeping records & monitoring	12% (7/60)	22% (7/32)
3. Organising & transforming	7% (4/60)	19% (6/32)
4. Seeking social assistance	32% (19/60)	6% (2/32)
5. Self-testing	18% (11/60)	15% (5/32)
6. Environmental structuring	7% (4/60)	31% (10/32)
7. Critical thinking	5% (3/60)	22% (7/32)
8. Outsourcing material	30% (18/60)	0% (0/32)
B. Will you change any of your study strategies for the next assessment?
Yes	68% (19/28)	56% (9/16)
No	32% (9/28)	44% (7/16)

Students could report more than one strategy. Difference response counts due to some students failing to answer both parts of Q39: Thinking about your study strategies and whether you think they worked for you, what do you feel has been the most effective study strategy you have used? Will you change any of your study strategies for the next assessment?

Table 3. Results of mixed linear modelling of changes between subscale scores at the start of semester 1 2018 (T1), end semester 1 2018 (T2) and start semester 2 2020 (T3) with Tukey’s Multiple comparisons with details of Cronbach’s alpha coefficients for survey subscales and knowledge survey. Little’s Missing Completely At Random (MCAR) test results and Chi-squared matching between timepoints shown.

				Semester 1, 2018 (1^st year)									Semester 2, 2020 (3^rd year)
				T1 (n = 163)					T2 (n = 109)				T3 (n = 39)
				Mean	SD		α		Mean	SD		α	Mean	SD	α
Self-efficacy				3.77	0.69		0.75		3.53	0.87		0.84	4.23	0.53	0.73
Academic self-handicapping ^a				3.81	0.78		0.59		3.60	0.83		0.63	3.74	0.83	0.74
Learning strategies				3.47	0.44		0.86		3.27	0.47		0.88	3.67	0.32	0.62
Time & study ^a				3.54	0.63		0.30		3.04	0.66		0.43	3.27	0.78	0.73
ATAR ^b				79.80	7.05				79.79	7.70			80.43	6.91
Pairwise differences
	Matching			T1 v T2					T1 v T3				T2 v T3
	Chi-square, df	p-Value	MCAR p-value	Mean Diff.	SE of Diff.	p-Value			Mean Diff.	SE of Diff.	p-Value		Mean Diff.	SE of Diff.	p-Value
Self-efficacy	18,29, 1	<0.001	0.238	0.24	0.074	0.005			−0.47	0.066	<0.001		−0.71	0.093	<0.001
Learning strategies	21.14, 1	<0.001	0.956	0.20	0.044	<0.001			−0.21	0.049	<0.001		−0.41	0.041	<0.001

Acceptable (a > 0.70) coefficients in bold.

Statistical significance for correlations at p < 0.05.

^aSubscale excluded from analysis due to low internal consistency (a < 0.70).

^bOne-way repeated measures ANOVA non-significant (p = 0.885).

Figure 2. Scores for self-efficacy (a) and learning strategies (b) subscales on the SRL questionnaire deployed to students at the start (T1) (n = 163) and end (T2) (n = 147) of semester 1, 2018, and at the start of semester 2, 2020 (T3) (n = 46). All questionnaire items assessed on a 5-point scale (1 = not at all true of me, 5 = very true of me). Mixed linear model with Tukey’s multiple comparisons used, comparing pairwise mean differences between scores at each timepoint, *p < 0.05, **p < 0.01. Means (solid lines) ± SD (dotted lines). All p-values presented in Table 3.

Figure 3. Regression of average mark for semester 1, 2018 and semester 1, 2020 with Australian Tertiary Admissions Rank (ATAR) (a) (n = 120, 2018; n = 105, 2020), knowledge score (b) (n = 124, 2018; n = 105, 2020), self-efficacy (c) and learning strategies (d) (both: n = 108, 2018; n = 35, 2020). Knowledge scores from start semester 1 2018 (T1 only) assessed confidence in scientific material on a 3-point scale (1 = I don’t feel confident I could answer this question, 3 = I feel confident I could answer the question). Self-efficacy and learning strategies subscales assessed on a 5-point scale (1 = not at all true of me, 5 = very true of me). Significantly non-zero correlations at p < 0.05. All p-values and R2 values presented in Table 4.

Table 4. Results of regression of variables Australian Tertiary Admissions Rank (ATAR), knowledge score and ‘Self-efficacy and ‘Learning strategies’ subscale scores against average mark for semester 1 2018 and semester 1 2020. Regression of normalised change in subscale scores between start semester 1, 2020 (T1) and start semester 2, 2020 (T3) against normalised change in average mark also shown.

	Regression of columned variable and average mark
	Semester 1, 2018 (1^st Year)				Semester 2, 2020 (3^rd Year)
	R^2		p-Value		R^2	p-Value	Regression of columned variable and normalised change in average mark ^a
ATAR	0.12		<0.001		0.06	0.011
Knowledge score	0.09		<0.001		0.10	0.002
	T1		T2		T3		T1 → T3
	R^2	p-Value	R^2	p-Value	R^2	p-Value	Mean change	SD	R^2	p-Value
Self-efficacy	<0.01	0.579	<0.01	0.797	<0.01	0.846	+ 0.33	0.38	0.16	0.068
Learning strategies	<0.01	0.766	0.01	0.478	0.02	0.390	+0.16	0.26	0.13	0.100

Statistical significance for correlations where p < 0.05.

^aMean cohort normalised change in average mark T1 → T3 was 0.53 ± 0.34.

Figure 4. Regression of normalised change in average mark between semester 1, 2018 and semester 2, 2020 with normalised change in self-efficacy (a) (n = 18) and learning strategies (b) (both n = 22) subscales between T1 and T3. Normalised change scores range from −1 to +1, with a score of +1 indicating the maximum possible gain. Four students with maximum score (score = 5) at T1 for self-efficacy were excluded from the analysis. Significantly non-zero correlations at p < 0.05. All p-values and R2 values presented in Table 4.

Nota, Laura, Salvatore Soresi, and Barry J. Zimmerman. 2004. “Self-Regulation and Academic Achievement and Resilience: A Longitudinal Study.” International Journal of Educational Research 41 (3): 198–215. doi:10.1016/j.ijer.2005.07.001.

 Google Scholar